Abstract
Statistical re-sampling techniques have been used extensively and successfully in the machine learning approaches for generation of classifier and predictor ensembles. It has been frequently shown that combining so called unstable predictors has a stabilizing effect on and improves the performance of the prediction system generated in this way. In this paper we use the re-sampling techniques in the context of Principal Component Analysis (PCA). We show that the proposed PCA ensembles exhibit a much more robust behaviour in the presence of outliers which can seriously affect the performance of an individual PCA algorithm. The performance and characteristics of the proposed approaches are illustrated on a number of experimental studies where an individual PCA is compared to the introduced PCA ensemble.
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© 2006 Springer-Verlag Berlin Heidelberg
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Gabrys, B., Baruque, B., Corchado, E. (2006). Outlier Resistant PCA Ensembles. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_55
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DOI: https://doi.org/10.1007/11893011_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46542-3
Online ISBN: 978-3-540-46544-7
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